Demand Response Programs in Multi-Energy Systems: a Review

energies

Review Demand Response Programs in Multi-Energy Systems: A Review

Morteza Vahid-Ghavidel 1,2 , Mohammad Sadegh Javadi 2 , Matthew Gough 1,2, Sérgio F. Santos 2 , Miadreza Shaﬁe-khah 3 and João P.S. Catalão 1,2,*

1 Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal; [email protected] (M.V.-G.); [email protected] (M.G.) 2 Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal; [email protected] (M.S.J.); [email protected] (S.F.S.) 3 School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland; mshaﬁek@univaasa.ﬁ * Correspondence: [email protected]

Received: 22 July 2020; Accepted: 17 August 2020; Published: 21 August 2020

Abstract: A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.

Keywords: demand response; electricity; energy hub; energy management; gas; multi-carrier; optimization; smart grid

1. Introduction There are numerous reasons for the ongoing transition within the energy sector. These include major concerns about the emissions and other environmental impacts of fossil fuel combustion. Demand Response (DR) has emerged as a key solution to help enable this energy transition [1]. DR can effectively increase the active engagement by consumers within the energy sector [2]. The Department of Energy of the United States has reported that active participation of consumers, through load reduction or load shifting, could be a key solution for future decarbonized energy systems [3]. This reduction or shifting of load can be done through DR programs (DRP) [4]. To increase the flexibility of the energy system and enhance its competitiveness, it is necessary to encourage all types of DR resources, including small loads to participate in the DRPs [5,6]. However, some inelastic electricity consumers cannot participate in DRPs for numerous reasons, for instance, they have critical or must-run loads. Different types of the DR resources can be found in recent works that explain the examples for critical

Energies 2020, 13, 4332; doi:10.3390/en13174332 www.mdpi.com/journal/energies Energies 2020, 13, x FOR PEER REVIEW 2 of 17 Energies 2020, 13, 4332 2 of 17 that explain the examples for critical and must-run loads [7]. Therefore, relying on a single energy carrierand must-run is not appropriate loads [7]. Therefore, for future relying energy on asystems. single energy MESs carrier can solve is not this appropriate issue, and for also future have energy the abilitysystems. to MESsmaintain can the solve comfort this issue, of the and end also-user have during the ability the operation to maintain of the comfortDRP. As of MES thes end-user rely on numerousduring the energy operation carriers of the such DRP. as Aselectricity, MESs rely gas, on heating, numerous and energycooling carriers, there is such an increased as electricity, amount gas, ofheating, flexibility and within cooling, the there system is an, which increased can be amount harnessed of flexibility by the utility within to theallow system, a diverse which range can beof charnessedonsumers by to theparticipate utility to in allow DRPs a diverse [8]. The range consumers of consumers can us toe a participate multitude in of DRPs energy [8]. carriers The consumers to help meetcan use their a multitude needs of ofenergy energy and carriers comfort to help while meet participating their needs ofin energyDRPs and[9]. The comfort use whileof the participating energy hub (EH)in DRPs for [MESs9]. The is useessential of the, energysince the hub EH (EH) is usually for MESs employed is essential, to depict since the energy EH is distribution usually employed systems to thatdepict have energy well distribution-developed gas systems and electricity that have well-developed network model gass [10,11] and electricity. Figure 1 networkpresents modelsa depiction [10,11 of]. theFigure relationship1 presents links a depiction within a of power the relationship system, other links energy within carriers a power and system, DR features. other energy The figure carriers has theand power DR features. system The as figurethe central has the body, power the system generation, as the the central transmission body, the generation,and sub-transmission the transmission high voltageand sub-transmission (HV), distribution high voltage(medium (HV), (MV) distribution and low voltage (medium (LV)), (MV) from and low which voltage we can (LV)), see from the interactionwhich we can of several see the interactionsystems to ofsatisfy several a set systems of loads, to satisfywhether a setelectric, of loads, transport, whether heating electric, or transport, cooling. Theseheating systems or cooling. have a These multitude systems of inputs have, asuch multitude as natural of gas inputs, and suchhydrogen, as natural among gas others, and hydrogen,and these alsoamong have others, functional and these connections also have functionalwith other connections systems such with as other renewable systems energy such assources renewable (RES) energy and connectisources (RES)ons with and heating connections systems with, creating heating Combined systems, creating Heat and Combined Power (CHP) Heat and points. Power Key (CHP) factors points. that allowKey factors interoperability that allow interoperabilitybetween different between systems diff areerent smart systems-grid areenabling smart-grid technologies. enabling technologies.These allow greaterThese allow interaction greater interactionbetween the between different the systemsdifferent andsystems allow and the allow cre theation creation of strategies of strategies for the for optimizationthe optimization of the of thesystem system operation, operation, for for example, example, demand demand response response programs programs at at different different levels, levels, such as districts, buildings and integration of electric vehicles (EV).

Figure 1. Linkages between the various energy carriers within a power system. Figure 1. Linkages between the various energy carriers within a power system. The main contributions of this paper are as follows: The main contributions of this paper are as follows: Review and synthesis of recent DRPs in MESs •  ReviewExploration and ofsynthesis the main of DRrecent in Energy DRPs in Hub MESs System models and their optimization. •  ExplorationUse of bibliometric of the main techniques DR in Energy to examine Hub System emerging models trends and within their optimization. the DRP in MES research •  Usearea of to bibliometric help display techniques the relation to ofexamine the topics emerging that have trends been within done the in thisDRP area. in MES This research shows area how tocertain help display topics have the relation emerged of overthe topics the years that have and highlightsbeen done in the this direction area. This in whichshows thehow research certain topicsis going. have The emerged organization over the of theyears paper and ishighlights the following: the directio first, an detailedin which review the research of recent is going DRPs. Theis presented. organization Then of the the energy paper hubis the concept, following as a: criticalfirst, a way detailed of enabling review MES, of recent is introduced, DRPs is presentedand the benefits. Then ofthe energy energy hubs hub are concept also presented., as a critical Following way of this,enabling DR programs MES, is thatintroduced use MES, and are thereviewed benefits and of discussedenergy hubs with are special also presented. attention beingFollowing paid this, to the DR di ffprogramserent optimization that use MES models are reviewedused. A scientometric and discussed analysis with special of the relevant attention literature being paid is then to conductedthe different to investigateoptimization key models trends used.and important A scientometric research analysis questions. of Thethe relevant paper then literature concludes is then with conducted the identification to investigate of some key key trendsissues andand theimportant presentation research of future questions. research The directions. paper then concludes with the identification of some key issues and the presentation of future research directions.

Energies 2020, 13, 4332 3 of 17

2. Demand Response This section introduces demand response, provides a definition of DR, and presents the various types of DRPs that have been implemented. Demand Response can provide valuable solutions to several of the challenges that the current power system is facing. These challenges include managing load demand from consumers for the ongoing electrification of our society [12]. This increasing load demand could cause some major problems to system operators, especially in peak periods. To meet these challenges, numerous solutions other than DR have been proposed, including optimization of renewable energy generators, storage units or capacitors. Another tool for tackling the issues related to increased load demand is network reconfiguration [1]. This involves better management of the power flow within the network to reduce congestion. DRPs offer great promise to help tackle the challenges faced by modern power systems. Before DR is discussed further, it is important to provide a robust definition of DR. Three definitions of DR have been identified for this purpose. One of these definitions is as follows: ‘changes in the electricity usage pattern of the consumers if the prices of the electricity would change in several periods’ [13]. DR can also occur through incentive payments provided to consumers to help modify both the magnitude and timing of their energy use. The other definition of the DR is the incentive payments that are createdEnergies to 2020 encourage, 13, x FOR PEER the consumersREVIEW to use less electricity when market derived prices are elevated,4 of 17 or the power system’s reliability is endangered. These definitions, although similar, focus on different aspectsapply relatedthe EDRP to to DR, reduce making the theload definitions supply through presented interrupting complementary. the major part of the large consumers’ loadThese for a threeshort period, definitions and consumers of DR show will usually that there run aretheir di backupfferent power objectives during for this DR period programs. [14]. TheseIn addition objectives to this, can there be reached is another through way to types maintain of DR the actions energy [14 system’s,15]. These reliability include and managingstability, which is to implement bulk load shedding. Consumers’ loads are curtailed instead of offering them load profiles through elasticity peak shaving and valley filling [13]. These results can be brought about monetary incentives. The above-mentioned incentive-based programs are not the only available by the use of time-dependent tariffs [16]. However, price is not always enough to convince a consumer DRPs. Other incentive-based programs have been created to meet the needs of various kinds of to actively participate in DRPs [17]. Two main branches of DRP are prevalent, and these are shown in consumer under different conditions, such as capacity markets, service markets, and demand Figure2, and these two branches are price-based and incentive-based DRPs [18]. bidding/buyback programs [14].

FigureFigure 2.2. IllustrationIllustration of several DRPs DRPs..

3. EnergyWithin Hubs price-based for Multi DRPs,-Energy fluctuations Systems Management in electricity prices are the main tool used to modify consumer energy use [19]. Within this category, there are three main programs, and these are Time of An exact definition of an energy hub could be as follows: a system that has the capability of Use (TOU), Critical Peak Pricing (CPP), and Real-Time Pricing (RTP) [20]. In contrast to price-based converting, storing and managing multiple energy carriers [24]. Another definition of energy hubs DRPs, incentive-based DRPs aim to modify consumer energy demand through the use of rewards [21]. which is more precise is introduced in [25]. In this paper, an energy hub is classified as an interface Within this category of DRPs, the most popular tools used include Direct Load Control (DLC), between various inputs and outputs of the energy carriers as well as converting and storing them. interruptible/curtailable services, emergency demand response programs (EDRP), ancillary services Therefore, an energy hub is the main component of an MES. markets,The capacity energy markets,hub concept and was demand formulated bidding by/buyback a team at programs. ETH Zurich The through most important the VOFEN of project. these are discussedIn this project, in the nextthe future’s section. energy market design for the next 20–30 years was created through a greenfieldA pricing design strategy of the based energy on system, TOU usesthat is di, tofferent assume blocks no effects of electricity of legacy pricesinfrastructure corresponding [26]. Theto diffprojecterent’s periods main highlights of the day. were In as TOU follows programs, [27]: electricity prices can be increased during periods of high demand or reduced during periods of flow demand to modify consumer behaviour [22]. 1. To gain more benefits from various energy carriers, a movement toward MESs is required. 2. Energy systems are required to implement non-hierarchical structures. 3. Energy systems are required to be more interconnected and more integrated. M. Geidl et al. presented the important outcomes of the VOFEN project in [24], which led to the above-mentioned goals. The first concept is an energy interconnector, and the second concept is the energy hub. Following the definition of an energy interconnector, several energy carriers are combined into just one transmission system for long-distance operation [28]. In the VOFEN project, the energy hub was defined and presented as follows: the energy hub is a direct or indirect interface that exists between the demand side and generation side, as well as the storage and transmission devices in various models, and it manages one or more energy carriers. In [29], the authors proposed a nonlinear model for handling the combined power flow optimization in the presence of several energy carriers including electricity, gas and heating systems that are based on the concept of the energy hub. They introduced a matrix as the model of an energy hub to simulate an optimization model for optimal power flow within energy hubs. Thus, the main energy hub framework is provided in [30] with the aim of MES management. There are several encouraging reasons to employ MESs instead of single energy systems. For instance, in [31], the use of the MESs led to sufficient improvement in the operation of a microgrid. Moreover, employing the energy hub in the energy system in [32] would improve the balance between the energy generation and demand, as well as smoothing the total consumer’s load profile

Energies 2020, 13, 4332 4 of 17

These programs generally do not change the amount of energy demanded but rather just shift the time of energy use to better suit the system status. CPP programs rather define the peak price period where the system reliability may be at risk. Electricity prices are kept high during this period to reduce consumption and thus reduce the risk on the system. In RTP programs, prices are determined through real-time market interactions to match supply and demand. Within incentive-based DRP programs, DLC programs give the utility the power to directly control various appliances owned by the consumer including electric water heaters, air-conditioners (AC) or various pumps. During periods of high demand, the utility can effectively shut these appliances off while rewarding the consumers through incentives [23]. The consumers can be involved directly in the DRPs through interruptible/curtailable services. The main aim of these programs is to have an agreement with the consumers to decrease the energy usage during the peak periods. In return, they receive monetary incentives. Likewise, in order to compensate the contingencies and enhance the reliability of the system, EDRP is being addressed. Transmission line failure or an outage in a generator could lead to insufficient generation during peak period and thus a system contingency can happen. Under these conditions, the best decision is to apply the EDRP to reduce the load supply through interrupting the major part of the large consumers’ load for a short period, and consumers will usually run their backup power during this period [14]. In addition to this, there is another way to maintain the energy system’s reliability and stability, which is to implement bulk load shedding. Consumers’ loads are curtailed instead of offering them monetary incentives. The above-mentioned incentive-based programs are not the only available DRPs. Other incentive-based programs have been created to meet the needs of various kinds of consumer under different conditions, such as capacity markets, service markets, and demand bidding/buyback programs [14].

3. Energy Hubs for Multi-Energy Systems Management An exact definition of an energy hub could be as follows: a system that has the capability of converting, storing and managing multiple energy carriers [24]. Another definition of energy hubs which is more precise is introduced in [25]. In this paper, an energy hub is classified as an interface between various inputs and outputs of the energy carriers as well as converting and storing them. Therefore, an energy hub is the main component of an MES. The energy hub concept was formulated by a team at ETH Zurich through the VOFEN project. In this project, the future’s energy market design for the next 20–30 years was created through a greenfield design of the energy system, that is, to assume no effects of legacy infrastructure [26]. The project’s main highlights were as follows [27]:

1. To gain more beneﬁts from various energy carriers, a movement toward MESs is required. 2. Energy systems are required to implement non-hierarchical structures. 3. Energy systems are required to be more interconnected and more integrated.

M. Geidl et al. presented the important outcomes of the VOFEN project in [24], which led to the above-mentioned goals. The first concept is an energy interconnector, and the second concept is the energy hub. Following the definition of an energy interconnector, several energy carriers are combined into just one transmission system for long-distance operation [28]. In the VOFEN project, the energy hub was defined and presented as follows: the energy hub is a direct or indirect interface that exists between the demand side and generation side, as well as the storage and transmission devices in various models, and it manages one or more energy carriers. In [29], the authors proposed a nonlinear model for handling the combined power flow optimization in the presence of several energy carriers including electricity, gas and heating systems that are based on the concept of the energy hub. They introduced a matrix as the model of an energy hub to simulate an optimization model for optimal power flow within energy hubs. Thus, the main energy hub framework is provided in [30] with the aim of MES management. Energies 2020, 13, 4332 5 of 17

There are several encouraging reasons to employ MESs instead of single energy systems. For instance, in [31], the use of the MESs led to sufficient improvement in the operation of a microgrid. Moreover, employing the energy hub in the energy system in [32] would improve the balance between the energy generation and demand, as well as smoothing the total consumer’s load profile through the implementation of the Stackelberg game approach. The importance of the energy hub is described in [33], where the authors state that it plays a major role in increasing the efficiency of the system, and additionally, it can decrease the operational investments costs. In addition, the characteristics of MESs that are dependent on several parameters, such as cost and emissions, could make the optimal dispatch of various energy sources, such as electricity and gas, possible. Thus, the use of multiple generation systems can be made more efficient by employing EH instead of conventional energy systems, since it can optimize energy usage, enhance efficiency, and decrease system costs and emissions. In some recent works, such systems have also been defined as integrated EH [34]. Moreover, two other significant advantages of MESs are listed as follows:

1. Increase in the system’s reliability: the reliability of the MES is generally higher than single energy systems, as the risk of depending solely on one source of energy is reduced. On the other hand, the individual energy systems’ reliability could be decreased, since the availability for the loads is still high. 2. The EH supply is optimized through the additional degree of freedom, since it is possible to characterize the inputs of the EH based on their availability, cost, emissions and other related factors. Hence, the dispatch of the EH’s inputs can be more optimally performed based on these characteristics [24].

4. Demand Response Modelling in Energy Hub Systems In this section, a specific focus is placed on the operation of DR within Energy Hubs. Firstly,the methods used to model the DRP and the Energy Hubs are discussed, and then following this, the different optimization techniques most commonly used are presented.

4.1. Modelling Energy hubs can be modelled in two ways in the power system. One of these methods is the energy flow method [35]. This type of energy hub modelling is shown in Figure3. This figure shows that the equations can be obtained from the usual power system’s energy flow. In these models, the inputs to an EH are generally electricity and natural gas, and on the other side, the output ports can provide electrical, heating and cooling energies simultaneously in this type of modelling, as the several types of energy carriers are coupled together. Thus, all the energy balancing equations, such as electrical, cooling and heating, have to be taken into account simultaneously [36,37]. The other approach for modelling energy hub equations uses concepts related to graph theory and is termed matrix-oriented energy hub modelling [38]. In this method, the energy converters are used to convert between various types of energy. To model the EH based on the matrix-based framework, it is necessary to define some of the following components in advance. These components include energy flow resistance, node, graph, branch, branch-node incidence matrix, and branch energy flow impedance matrix. The definition of these components is described in [39]. For instance, to PDR j model DR in this approach, [39] considered some assumptions that are required: l,j indicates the -th load branch after DR. Therefore, the directed graph of the EH using this approach can be depicted in Figure4. Energies 2020, 13, x FOR PEER REVIEW 5 of 17

through the implementation of the Stackelberg game approach. The importance of the energy hub is described in [33], where the authors state that it plays a major role in increasing the efficiency of the system, and additionally, it can decrease the operational investments costs. In addition, the characteristics of MESs that are dependent on several parameters, such as cost and emissions, could make the optimal dispatch of various energy sources, such as electricity and gas, possible. Thus, the use of multiple generation systems can be made more efficient by employing EH instead of conventional energy systems, since it can optimize energy usage, enhance efficiency, and decrease system costs and emissions. In some recent works, such systems have also been defined as integrated EH [34]. Moreover, two other significant advantages of MESs are listed as follows: 1. Increase in the system’s reliability: the reliability of the MES is generally higher than single energy systems, as the risk of depending solely on one source of energy is reduced. On the other hand, the individual energy systems’ reliability could be decreased, since the availability for the loads is still high. 2. The EH supply is optimized through the additional degree of freedom, since it is possible to characterize the inputs of the EH based on their availability, cost, emissions and other related factors. Hence, the dispatch of the EH’s inputs can be more optimally performed based on these characteristics [24].

4. Demand Response Modelling in Energy Hub Systems In this section, a specific focus is placed on the operation of DR within Energy Hubs. Firstly, the methods used to model the DRP and the Energy Hubs are discussed, and then following this, the different optimization techniques most commonly used are presented.

4.1. Modelling Energy hubs can be modelled in two ways in the power system. One of these methods is the energy flow method [35]. This type of energy hub modelling is shown in Figure 3. This figure shows that the equations can be obtained from the usual power system’s energy flow. In these models, the inputs to an EH are generally electricity and natural gas, and on the other side, the output ports can provide electrical, heating and cooling energies simultaneously in this type of modelling, as the severalEnergies 2020 types, 13 ,of 4332 energy carriers are coupled together. Thus, all the energy balancing equations, 6such of 17 as electrical, cooling and heating, have to be taken into account simultaneously [36] and [37].

PV E Storage Electrical Load Transformer

Heater Heating CHP Load

Energies 2020, 13, x FOR PEER REVIEW 6 of 17

Gas used to convert between various types of energy. To model the EH basedCooling on the matrix-based Load framework, it is necessary to define some Boilerof the followingChiller components in advance. These components include energy flow resistance, node, graph, branch, branch-node incidence matrix, and branch energy flow impedance matrix. The definition of these components is described in [39]. For instance, H Storage 퐷푅 to model DR in this approach, [39] considered some assumptions that are required: 푃푙,푗 indicates the j-th load branch after DR. Therefore, the directed graph of the EH using this approach can be FigureFigure 3. IllustrationIllustration of of the the EH EH modelled modelled based based on on energy energy flow ﬂow.. depicted in Figure 4. The other approach for modelling energy hub equations uses concepts related to graph theory PV Storage In and is termed matrix-oriented energy hub modelling [38]. In this method, the energy converters are Storage Out

Grid Electrical Load

Heater CHP Chiller Cooling Load Gas

Heating Load Boiler

H Storage Out H Storage In Figure 4. Illustration of the EH modelled based on matrix approach. Figure 4. Illustration of the EH modelled based on matrix approach. It has to be noted that most of the models dealing with DR in the EH are proposed using the first It has to be noted that most of the models dealing with DR in the EH are proposed using the first method, i.e., energy flow. For instance, in [40], the authors proposed an MES through the energy method, i.e., energy flow. For instance, in [40], the authors proposed an MES through the energy flow flow method that consists of boilers (including heat recovery, gas and electric), a heat exchange method that consists of boilers (including heat recovery, gas and electric), a heat exchange device, an device, an absorption chiller, an electric heat pump, energy storage (both electric and heat energy), absorption chiller, an electric heat pump, energy storage (both electric and heat energy), and and generation technologies (including micro gas turbine, wind turbine, and photovoltaic units). In this generation technologies (including micro gas turbine, wind turbine, and photovoltaic units). In this work, flexible DR for the electrical and heating loads are implemented. Similarly, energy flow is used work, flexible DR for the electrical and heating loads are implemented. Similarly, energy flow is used in [41] in the MES model considering DR to provide more flexibility for consumers. The DR program in [41] in the MES model considering DR to provide more flexibility for consumers. The DR program that is applied in this study uses TOU and considers three representative periods over the course of a that is applied in this study uses TOU and considers three representative periods over the course of day, i.e., peak, valley, and flat hours. a day, i.e., peak, valley, and flat hours. 4.2. Optimization 4.2. Optimization 4.2.1. Algorithms Used to Solve 4.2.1. Algorithms Used to Solve There have been several methods and algorithms that could be employed in the models to find theThere solution have for been the problemsseveral methods defined. and In [algorithms34], since the that model could was be aemployed bilevel optimization in the models scheduling to find the solution for the problems defined. In [34], since the model was a bilevel optimization scheduling model, the proposed optimization short-term model results in a non-linear problem that is formulated in two levels. There are some main approaches to find the solution, such as the branch and bound method, Karush–Kuhn–Tucker (KKT), the penalty function method, etc. In this work, it is possible to determine the decision variables within the bounds of the constraints after some simplification. Thus, the lower level KKT conditions can be used as the upper level’s constraints. Therefore, the bilevel model is reformulated into a corresponding single-level problem. Likewise, the solution that is considered in [42] led to binary mixed-integer linear programming problems. To manage the coupling variables in the constraints, a binary variable is considered. In this work, linear interactive and general optimizer (Lingo) software was used to find the optimal solution. There are some heuristic methods that have been applied as solution methods to find the optimization point. For instance, a Stackelberg Game Approach was taken in [32]. Other heuristic

Energies 2020, 13, 4332 7 of 17 model, the proposed optimization short-term model results in a non-linear problem that is formulated in two levels. There are some main approaches to find the solution, such as the branch and bound method, Karush–Kuhn–Tucker (KKT), the penalty function method, etc. In this work, it is possible to determine the decision variables within the bounds of the constraints after some simplification. Thus, the lower level KKT conditions can be used as the upper level’s constraints. Therefore, the bilevel model is reformulated into a corresponding single-level problem. Likewise, the solution that is considered in [42] led to binary mixed-integer linear programming problems. To manage the coupling variables in the constraints, a binary variable is considered. In this work, linear interactive and general optimizer (Lingo) software was used to find the optimal solution. There are some heuristic methods that have been applied as solution methods to find the optimization point. For instance, a Stackelberg Game Approach was taken in [32]. Other heuristic approaches can also be employed to find the best solution is genetic algorithms. In [43], the authors searched for the best price in the microgrid through a genetic algorithm.

1. Beneﬁt Maximization

In some models, the optimization aims to maximize the profit. For instance, the objective of [44] was to maximize the profit of the electricity utility company. The proposed optimization model of [45] comprises two objectives, whereby one of them was to maximize the profit. The objective function in [5] sought to maximize the expected profit of the aggregator.

2. Cost Minimization

There are some studies in this field in which their optimization goal is to minimize the cost. In [46], the objective function of this optimization problem was the minimization of the incentive payment, while considering the load-shaving constraints. In [47], the main objective in the upper level was to minimize the total cost of operation of an EH, while maximizing the exergy efficiency in the lower level. Minimizing the cost of the considered energy system, as was done in [48], was the main aim of this optimization problem, in consideration of electricity and heat loads, energy carriers’ tariffs, converter efficiency, and the starting time of the customers’ preferred appliances. The MES is based on the minimum system costs.

5. The Direction of the Research This section will examine the most recent studies that focus on DR in MESs, and present a synthesized overview of the present state of the art. Following this, a bibliometric study will be carried out to investigate the emerging trends within the MES and DR research area.

5.1. Main Contributions of the Studied Works The area of DRP in multi-energy systems has been receiving increasing interest from the scientific community in recent years, as shown in Figure5. This figure shows the number of studies published in which the main area is the application of DRP in multi-energy systems. Based on this figure, the number of publications has been increasing in an exponential trend since 2016, which clearly shows the current relevance of the topic. Energies 2020, 13, x FOR PEER REVIEW 7 of 17 approaches can also be employed to find the best solution is genetic algorithms. In [43], the authors searched for the best price in the microgrid through a genetic algorithm.

4.2.2. The Main Objective of the Optimization In general, two separate objectives can be modelled. These are: maximizing the benefit to the participants, mainly through maximising their profit; or minimizing their cost. 1. Benefit Maximization In some models, the optimization aims to maximize the profit. For instance, the objective of [44] was to maximize the profit of the electricity utility company. The proposed optimization model of [45] comprises two objectives, whereby one of them was to maximize the profit. The objective function in [5] sought to maximize the expected profit of the aggregator. 2. Cost Minimization There are some studies in this field in which their optimization goal is to minimize the cost. In [46], the objective function of this optimization problem was the minimization of the incentive payment, while considering the load-shaving constraints. In [47], the main objective in the upper level was to minimize the total cost of operation of an EH, while maximizing the exergy efficiency in the lower level. Minimizing the cost of the considered energy system, as was done in [48], was the main aim of this optimization problem, in consideration of electricity and heat loads, energy carriers’ tariffs, converter efficiency, and the starting time of the customers’ preferred appliances. The MES is based on the minimum system costs.

5.1. Main Contributions of the Studied Works The area of DRP in multi-energy systems has been receiving increasing interest from the scientific community in recent years, as shown in Figure 5. This figure shows the number of studies published in which the main area is the application of DRP in multi-energy systems. Based on this figure, the number of publications has been increasing in an exponential trend since 2016, which Energies 2020, 13, 4332 8 of 17 clearly shows the current relevance of the topic.

40 35 30 25 20 15

10 Number of Publications of Number 5 0 2002 2013 2014 2015 2016 2017 2018 2019

Energies 2020, 13, x FOR PEER REVIEW Year of publication 8 of 17 Figure 5. Year of the studied publications. The types of publication dealingFigure 5. with Year DRPof the instudied multi publications-energy systems. are illustrated in Figure 6. FromThe the types figure, of publicationit is possible dealing to see with that theDRP largest in multi-energy number of systems works are are published illustrated in in scientific Figure6. Fromjournals, the ﬁgure,i.e., more it is than possible 60% to of see the that reviewed the largest works. number However, of works there are are published other types in scientiﬁc of publications journals,, i.e.,such more as presentation than 60% of of the proposed reviewed DR works. models However, at international there are other conferences, types of in publications, books, and suchin PhD as presentationdissertations. of proposed DR models at international conferences, in books, and in PhD dissertations.

40 35 30 25 20 15 10 5 0 BK CNF JRL THESIS

Figure 6. Type of the studied publications. Figure 6. Type of the studied publications. Each one of the existing publications focuses on different aspects, combinations of sources, and technologies,Each one of the from existing MESs publications and DR through focuses to approacheson different andaspects, models. combinations In this sense, of sources in Table, and1, atechnologies, set of works from published MESs inand recent DR through years is to presented, approaches having and beenmodels. classified In this accordingsense, in Table to the 1, main a set areaof works of the published paper, which in recent MES years sources is presented, are considered, having i.e. been cooling classified (CL), according heating (HT),to the powermain area (PW), of waterthe paper, (WR), which gas (GS). ME AndS source the presences are considered of distributed, i.e. cooling energy (CL), resources heating (DER), (HT), plugin power electric (PW), vehicles water (PEV),(WR), gaselectrical (GS). storageAnd the systems presence (ES), of distributedand Hydrogen energy storage resources (HS). For (DER) each, plugin work, electric the main vehicles focus and(PEV the), electrical respective storage area systems of contribution (ES), and are Hydrogen highlighted. storage Figure (HS). 7For illustrates each work, the the area main of focus and of thethe publications respective area studied of contributionin Table1. According are highlighted. to this figure, Figure it is 7 shown illustrates that most the of area the of studied focus works of the focuspublications on EH as studied the main in areaTable of 1. research. According Additionally, to this figure, the simulation it is shown environments that most of ofthe the studied optimization works modelfocus on of the EH models as the which main use area DR of in research. the multi-energy Additionally carrier, the systems simulation is described. environments Themodels of the canoptimization be implemented model of in the several models programs, which us suche DR as in the the General multi-energy Algebraic carrier Modelling systems Systemis described. (GAMS), The MATLAB,models can etc. be Theimplemented simulation in environment several programs (S.E) in, such Table as1 showsthe General the employed Algebraic program. Modelling System (GAMS), MATLAB, etc. The simulation environment (S.E) in Table 1 shows the employed program.

10 Number of Publications of Number 5

0 Multi-Energy (ME) Demand Response (DR) Energy-hib (EH)

Figure 7. The main area of the works in Table 1

Energies 2020, 13, 4332 9 of 17

Table 1. The most recent studied works.

Ref. Main Research Area S.E. CL HT PW WR GS DER PEV ES HS Ref. Main Research Area S. E. CL HT PW WR GS DER PEV ES HS [8] EH MATLAB [11] EH MATLAB × × × × × × × × × [31] EH GAMS [32] DR MATLAB × × × × × × × × × [34] EH MATLAB [35] ME GAMS × × × × × × × × × × [36] EH GAMS [39] EH MATLAB × × × × × × × × × × × [41] ME NA [42] ME LINGO × × × × × × × × × × [43] HE MATLAB [44] DR NA × × × × × × × × [45] DR GAMS [46] EH NA × × × × × × × [47] EH GAMS [48] DR NA × × × × × × × × × [49] ME NA [50] DR GAMS × × × × × × × × [51] EH GAMS [52] EH GAMS × × × × × × × × × × [53] ME NA [54] DR MATLAB × × × × × × × × × [55] EH MATLAB [56] ME GAMS × × × × × × × × × × × [57] ME NA [58] DR GAMS × × × × × × × × × × [59] EH MATLAB [60] EH GAMS × × × × × × × × × [61] EH GAMS [62] ME GMAS × × × × × × × × × × [63] ME MATLAB [64] EH MATLAB × × × × × × × [65] ME MATLAB [66] ME MATLAB × × × × × × × × × × × × × × [67] EH GAMS [68] ME NA × × × × × × × × × × × × [69] DR NA [70] EH GAMS × × × × × × × × × × [71] EH GAMS [72] EH GAMS × × × × × × × × × × × × [73] DR MATLAB [74] DR MATLAB × × × × × × × [75] ME MATLAB [76] EH GAMS × × × × × × × × × [77] ME GAMS [78] EH GAMS × × × × × × × × × × × [79] EH NA [80] DR GAMS × × × × × × × × [81] EH GAMS [82] DR MATLAB × × × × × × × × × × Energies 2020, 13, x FOR PEER REVIEW 8 of 17

The types of publication dealing with DRP in multi-energy systems are illustrated in Figure 6. From the figure, it is possible to see that the largest number of works are published in scientific journals, i.e., more than 60% of the reviewed works. However, there are other types of publications, such as presentation of proposed DR models at international conferences, in books, and in PhD dissertations.

40 35 30 25 20 15 10 5 0 BK CNF JRL THESIS

Figure 6. Type of the studied publications.

Each one of the existing publications focuses on different aspects, combinations of sources, and technologies, from MESs and DR through to approaches and models. In this sense, in Table 1, a set of works published in recent years is presented, having been classified according to the main area of the paper, which MES sources are considered, i.e. cooling (CL), heating (HT), power (PW), water (WR), gas (GS). And the presence of distributed energy resources (DER), plugin electric vehicles (PEV), electrical storage systems (ES), and Hydrogen storage (HS). For each work, the main focus and the respective area of contribution are highlighted. Figure 7 illustrates the area of focus of the publications studied in Table 1. According to this figure, it is shown that most of the studied works focus on EH as the main area of research. Additionally, the simulation environments of the optimization model of the models which use DR in the multi-energy carrier systems is described. The Energiesmodels2020 can, 13 be, 4332 implemented in several programs, such as the General Algebraic Modelling System10 of 17 (GAMS), MATLAB, etc. The simulation environment (S.E) in Table 1 shows the employed program.

10 Number of Publications of Number 5

0 Multi-Energy (ME) Demand Response (DR) Energy-hib (EH)

Figure 7. The main area of the works in Table1. Figure 7. The main area of the works in Table 1 Table1 presents a summarized list of all of the publications analysed for this review. The papers were classified according to the type of energy carrier analysed in the paper. These were grouped into five separate groups. Group A [32,36,39,45,47–54,56,59–65,67,70,71,76,80,82] contains papers which consider power, heat and gas. Group B [8,31,35,41,42,57,63,66,68,69,72,74,77,78,81] includes those papers which consider heat, power, gas and cooling. In Group C [34,43,46], the papers consider power heat and cooling. Group D [58,75] considers power and heat, and Group E [44,73] considers power and gas. Most of the works that consider power, heat and gas consider the presence of DERs and ESS, as is the case of [51,52,71]. In [71], a model is formulated for the ideal planning of an energy hub considering various operational constraints. The EH is comprised of various technologies such as a Transformer, a Combined Heat and Power (CHP) unit, Boiler, and Thermal Storage (TS). Further developments to the EH include a Wind Turbine (WT), Energy Storage (ES), and DRP. Energy carriers used as inputs for the hub include electricity, gas, and water. The authors in [51] focus on the optimum operation for a multi-carrier energy system considering the effects of a wind farm, storage (both electrical and thermal), DR programs using both electrical and thermal carriers, and markets for both electricity thermal energy. The authors use stochastic programming to model the associated uncertainties, including demand, market prices and wind speeds. In [52], a stochastic risk-constrained bilevel method is used to solve a scheduling problem for a smart energy hub. The results of this model would be the schedule corresponding to the least-cost 24-hour operation that would satisfy electrical and thermal demands using a combination of CHPs, boilers and storage systems. The set of works considering power, heat, gas and cooling follows the same approach as the previous group, but in these ones, the MES model considers the presence of cooling systems, some examples are the works presented in [8,66,78]. A long-term configuration and sizing model is developed in [8] with an integrated DR to investigate the optimal capacity of assets for an energy service company who operates energy hubs. The authors apply the concept of DR to both heating and cooling loads. These loads are assumed to be both curtailable and shiftable. The model is applied with the objective to enhance the economic efficiency and system flexibility. The paper offers an approach for the energy service company to evaluate the expected total cost including the investment costs as well as operational costs. A residential energy hub model is described in [66]. This model used electricity, natural gas and solar radiation as inputs to meet the required demand which included electrical, heating and cooling demands. Supplementing the operational flexibility of the system is also achieved by including a DR program that uses a combination of load shifting, load curtailing Energies 2020, 13, 4332 11 of 17 and flexible thermal load scheduling. In [78], the concept of the smart residential energy hub (SREH) is introduced. This hub deals with defining the optimal operations schedule to meet the various demands considering uncertainties relating to electricity market prices and solar radiation. In the group of power, heat and cooling, the works do not consider gas in the models, as can be seen in [34,43,46]. An example of this group is [46], where an interactive strategy for dispatching is developed. This strategy used information relating to bids from the demand side as well as coordination between multi-energy carriers to operate a virtual power plant (VPP) so as to augment system reserves. Using the concepts of a VPP allows for the dispatch strategy to meet the requirements of the system through the use of CCHP and interruptible loads. The remaining groups, power and heat ([58,75]) and power and gas ([44,73]) have these systems as a base; however, they address or integrate additional energy carriers, as is the case with EVs in [58,75]. In [58], the paper proposes a method for improved coordination between desalination plants, heating, and power systems. This coordination is carried out through the optimal scheduling of PEVs within the water-heat-power nexus. The authors in [75] develop a strategy to optimize a network of multi-energy hubs. These hubs include renewable generation, dispatchable energy sources, and energy storage through the use of vehicle to grid (V2G) technology. These devices operate within the hub based on a TOU tariff regime. A new line of research can be seen in [62,79], where hydrogen storage systems are considered in the MES model. In [79], a management mechanism for self-regulating DR is presented. This mechanism includes deferrable electrolysers, and these are used as the main controllable resource in a hydrogen-based clean energy hub. This hub also includes generation units, both traditional and low-carbon as well as wind turbines. This strategy uses the hysteresis control model to coordinate the aggregation of the electrolysers. The control model also includes constraints relating to comfort. An energy hub with multiple objective functions is presented in [62]. In this model, both the operational costs as well as the emissions are minimized. The emissions include those inside the hub as well as those outside the hub. The system uses wind turbines as well as DR. In addition, the model relies heavily on the hydrogen network within the hub. The hub considers hydrogen as one of the main outputs. The hub uses hydrogen storage, thermal DR, power-to-hydrogen, hydrogen-to-power, and gas-to-power, as well as the WT, to reach the objective of the lowest possible cost and emissions.

5.2. Trending Keywords in the Area To better understand the underlying trends in the studied literature, a scientometric analysis was conducted. VOSviewer, a well-known scientometric analysis software, was used to better understand the relationships present within the studied literature. In this analysis, a total of 75 academic articles were studied using VOSviewer 1.6.15 [83]. A total of 173 keywords were identiﬁed and the most prevalent keywords are shown in Figure4 below. As is expected, the DR and energy hubs are the main keywords. Integrated Demand Response is also prevalent in several articles, which could indicate that use of the integrated DR in the MESs is also one of the most recent areas of interest, and is being investigated in several studies, like [74,84]. In Figure8, the di ﬀerent colours used represent the year of publication. with the most recent publications shown in yellow, while the older publications are shown in purple. Energies 2020, 13, x FOR PEER REVIEW 2 of 17

This mechanism includes deferrable electrolysers, and these are used as the main controllable resource in a hydrogen-based clean energy hub. This hub also includes generation units, both traditional and low-carbon as well as wind turbines. This strategy uses the hysteresis control model to coordinate the aggregation of the electrolysers. The control model also includes constraints relating to comfort. An energy hub with multiple objective functions is presented in [62]. In this model, both the operational costs as well as the emissions are minimized. The emissions include those inside the hub as well as those outside the hub. The system uses wind turbines as well as DR. In addition, the model relies heavily on the hydrogen network within the hub. The hub considers hydrogen as one of the main outputs. The hub uses hydrogen storage, thermal DR, power-to-hydrogen, hydrogen-to-power, and gas-to-power, as well as the WT, to reach the objective of the lowest possible cost and emissions.

5.2. Trending Keywords in the Area To better understand the underlying trends in the studied literature, a scientometric analysis was conducted. VOSviewer, a well-known scientometric analysis software, was used to better understand the relationships present within the studied literature. In this analysis, a total of 75 academic articles were studied using VOSviewer 1.6.15 [83]. A total of 173 keywords were identified and the most prevalent keywords are shown in Figure 4 below. As is expected, the DR and energy hubs are the main keywords. Integrated Demand Response is also prevalent in several articles, which could indicate that use of the integrated DR in the MESs is also one of the most recent areas of interest, and is being investigated in several studies, like [74,84]. Energies 2020, 13, 4332 12 of 17 In Figure 8, the different colours used represent the year of publication. with the most recent publications shown in yellow, while the older publications are shown in purple.

FigureFigure 8. 8.Main Main keywords keywords andand the temporal relationship relationship between between them. them.

6. Conclusions6. Conclusions DueDue to theto the limited limited capabilities capabilities of of thethe currentcurrent power system, system, demand demand-side-side resources resources cannot cannot be be integratedintegrate veryd very easily. easily. Demand Demand Response Response helpshelps to overcome these these limitations limitations,, and and thus thus can can help help to to integrateintegrate more more demand-side demand-side resources. resources. ToTo capturecapture the full full potential potential of of these these resources, resources, DRPs DRPs should should consider various carriers of energy, such as electricity and natural gas. This can be achieved through the use of Energy Hubs. This will help to maximize the benefits of DRPs and also minimize the side effects, such as consumer discomfort. This is a major advantage of using MESs. In this paper, the main definitions of the DR and the energy hub are reviewed. Then, the advantages of the energy hub over the conventional power system are addressed. Then, some recent modelling of the DR in the energy hub environment is studied. The comprehensive review that has been done in this work can be a reference for future research and improvements in applying the DR in energy hub systems. The emerging keywords that have been extracted from the studied works show that “integrated DR” receives the most interest and is one of the main keywords that is linked to the energy hub topic. The work that has been done identified some challenges when it comes to implementing DRP in MESs, and these are as follows:

The capability of converting between various forms of energy: some limitations restrict • the possibility of converting between the different energy carriers across time and for different consumers. For instance, there are some consumers with must-run loads where the only available form of energy is electricity. Therefore, it is not possible to participate in the DRPs through the reviewed works. For an optimization model, it is suggested to consider both consumer discomfort and profit at • the same time, since there have been some studies that only focused on decreasing the discomfort rate of the consumers participating in the DRPs in MESs. On the other hand, the main aim of some studies is to increase the profit of the consumers through their participation in the DRPs in the MES. However, there is the capability of developing models that consider both the minimization of rate of discomfort of consumers while also increasing profit by employing DRPs. Energies 2020, 13, 4332 13 of 17

This work gathered and summarized the most recent work concerning Demand Response programs within MESs. It showed that there is a growing increase in the ﬁeld, and this is because of the several advantages that DR and MESs can contribute to future energy systems. Both will be important as the energy transition takes hold, and the combination of these two strategies can yield multiplicative advantages for both system operators and consumers. This work provided a summarized foundation for future researchers to consult when working in this exciting and important ﬁeld.

Author Contributions: Conceptualization, M.V.-G., S.F.S. and M.S.J.; methodology, M.V.-G., S.F.S. and M.S.J.; software, M.V.-G.and M.G.; validation, S.F.S. and M.S.J. and M.S.-k.; formal analysis, M.V.-G.,M.G., S.F.S. and M.S.J.; investigation, M.V.-G., S.F.S. and M.S.J.; resources, M.V.-G. and M.G.; data curation, M.V.-G.; writing—original draft preparation, M.V.-G., S.F.S. and M.S.J.; writing—review and editing, M.G. and J.P.S.C.; visualization, M.V.-G., S.F.S. and M.S.J.; supervision, M.S.-k. and J.P.S.C.; project administration, J.P.S.C.; funding acquisition, J.P.S.C. All authors have read and agreed to the published version of the manuscript. Funding: J.P.S. Catalão acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under POCI-01-0145-FEDER-029803 (02/SAICT/2017). Acknowledgments: The work of M. Shafie-khah was supported by FLEXIMAR-project (Novel marketplace for energy flexibility), which has received funding from Business Finland Smart Energy Program, 2017-2021. Conflicts of Interest: The authors declare no conflict of interest.

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